Human Trafficking: Addressing the Chiasmic Training Gap Through a Train-the-Trainers Model
Notice bibliographique
Résumé
Human trafficking is a global health problem, affecting the health of men, women, and children, and disproportionately affecting marginalized populations. At some point during their exploitation, those who experience trafficking will likely have contact with a health professional. 1 Unfortunately, many trafficking survivors report that contact with health professionals did not lead to safety and healing: their exploitation was not identified, they felt judged, or they feared arrest or/deportation. A holistic response to trafficking in the health care setting is critical, involving multidisciplinary coordination including social work, nursing, midlevel providers, and physicians. 2,3 The health care workforce is comprised of millions of practicing clinicians who have not been trained on responses to trafficking. Moreover, trafficking is not routinely a part of medical curricula. 4 Given the fact that trafficking is just beginning to come onto medicine’s radar, there are very few individuals with the combination of expertise in teaching skills and trauma-informed clinical trafficking approaches. 4 Simply put, there is a chiasmic gap between those health professionals who are and are not competent in caring for trafficked people and few people qualified to train them. 4 In 2019, 2020, and 2021, we conducted human trafficking health professional “train the trainers” for over 150 teachers. To promote a nonhierarchical environment of interprofessional learning, we used social cognitive constructivism, and experiential learning theory to scaffold participants’ knowledge. We leveraged technology to build community. Whatsapp and flipgrid connected participants and faculty before the program. Participants’ prior experiences informed confidential and respectful information sharing. Live case presentations were interwoven with prerecorded didactics, Zoom break-out case analyses, and Q&A sessions with trafficking survivors. Participants co-developed a didactic in groups for educating others about labor and sex trafficking, disclosure, and the law, facilitating integration of new knowledge with participants’ prior experiences and authentic work responsibilities. Groups then taught their didactics using newly acquired understanding of learning theory and received peer feedback on content, clarity, and engagement of their teaching. Pre–post retrospective surveys assessed changes in knowledge and skills, immediately and 3 months post program. As of 2021, over 150 physicians, medical students, nurses, social workers, public health workers, physician assistants, and psychologists from the United States, United Kingdom, Canada, and Trinidad/Tobago have graduated from the program. Three-month postprogram surveys indicated lasting, statistically significant behavior change in use of the SOAR framework, teaching with adult learning principles, and creating organizational trafficking protocols. Qualitative analysis revealed participants’ training in their communities has led to increased identification of patients experiencing trafficking. Our human trafficking health care “train-the-trainer” model empowers teachers to train others, improve their health systems, and community responses to trafficking. Our human trafficking health care “train-the-trainer” model can be scaled and implemented in regions around the United States to bridge the chiasmic gap between those health professionals who are and are not competent in caring for trafficked people and those qualified to train them.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,009 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».